基于GA-ELM的锂电池SOC估计及主动均衡

SOC Estimation and Active Equalization of Lithium Battery Based on GA-ELM

锂电池的状态估计和主动均衡是提高电池性能和延长使用寿命的关键技术,针对参数模型的荷电状态(State of charge,SOC)估计方法忽略电动汽车实际工况而导致的估计偏差较大的问题,提出一种基于遗传算法的极限学习机(GA-ELM)神经网络算法来估计电池的荷电状态SOC,通过遗传算法优化了ELM的参数,提高估计精度和泛化能力,并在UDDS工况数据下进行训练与测试。同时采用双向Buck-Boost均衡拓扑结构,该拓扑结构能够快速实现电池间的能量传递,同时又降低了传递路径的复杂性。通过遗传算法的极限学习机估计出的SOC作为均衡变量,利用Matlab/Simulink仿真平台进行试验。结果表明,提出的GA-ELM神经网络平均误差为0.15%,而传统的ELM神经网络平均误差为0.56%,因此提出的神经网络能够更精确地估计SOC;同时电池组之间能够快速完成能量均衡,证明了所提方案的可行性。

State estimation and active equalization of lithium batteries are key technologies to improve battery performance and extend service life.Aiming at the problem that state of charge(SOC)estimation method of parameter model ignores the actual working conditions of electric vehicles and causes large estimation deviation,an extreme learning machine(GA-ELM)neural network algorithm is proposed based on genetic algorithm to estimate SOC of battery.The parameters of ELM are optimized by genetic algorithm to improve the estimation accuracy and generalization ability,and the training and testing are carried out under UDDS working condition data.At the same time,the bidirectional Buck-Boost balanced topology can quickly transfer energy between cells,while reducing the complexity of the transfer path.The SOC estimated by the extreme learning machine of genetic algorithm is used as the equilibrium variable,and the experiment is carried out by Matlab/Simulink simulation platform.The results show that the average error of the GA-ELM neural network proposed in this paper is 0.15%,while that of the traditional ELM neural network is 0.56%.Therefore,the neural network proposed can estimate SOC more accurately.At the same time,the energy balance between the battery pack can be quickly completed,which proves the feasibility of the scheme.